A Deep Learning Approach for Dynamic Balance Sheet Stress Testing

TL;DR

This paper introduces a deep learning-based method for dynamic balance sheet stress testing, addressing limitations of traditional models by capturing non-linear risks more accurately and efficiently.

Contribution

It proposes a novel deep learning approach for balance sheet stress testing, improving realism and accuracy over existing satellite-model-based methods.

Findings

Outperforms traditional stress testing approaches

More accurately captures non-linear adverse shocks

Efficiently simulates real-world financial scenarios

Abstract

In the aftermath of the financial crisis, supervisory authorities have considerably altered the mode of operation of financial stress testing. Despite these efforts, significant concerns and extensive criticism have been raised by market participants regarding the considered unrealistic methodological assumptions and simplifications. Current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models. This renders their integration a really challenging task, leading to significant estimation errors. Moreover, advanced statistical techniques that could potentially capture the non-linear nature of adverse shocks are still ignored. This work aims to address these criticisms and shortcomings by proposing a novel approach based on recent advances in Deep Learning towards a principled method for Dynamic Balance Sheet Stress Testing. Experimental results on a newly collected financial/supervisory dataset, provide strong empirical evidence that our paradigm significantly outperforms traditional approaches; thus, it is capable of more accurately and efficiently simulating real world scenarios.